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Revolutionized real-time object detection by predicting both bounding boxes and class probabilities in one forward pass. A fast, accurate system that detects objects in real-time.
Introduced deep convolutional neural networks to the world with its success in the ImageNet challenge, dramatically reducing error rates and popularizing CNNs in AI.
Enabled training extremely deep neural networks by using residual blocks, significantly boosting performance in image recognition tasks.
Optimized for medical image segmentation, this architecture excels in tasks requiring precise localization and works well with very few training images.
Challenges the dominance of CNNs by applying the transformer architecture, originally designed for NLP, to image recognition tasks. Demonstrates that transformers can effectively handle pixels too.
These papers offer a foundation in understanding the breakthrough technologies that drive today's AI applications in Computer Vision. Dive deeper to unlock your potential in this exciting field.